S.Y. Grodzensky – Dr.Sc. (Eng.), Professor,

MIREA – Russian University of Technology (Moscow)

E-mail: grodzenskiy_s@mail.ru

A.N. Chesalin – Ph.D. (Eng.), Associate Professor,

MIREA – Russian University of Technology (Moscow)

S.A. Ovchinnikov – Ph.D. (Eng.), Associate Professor,

MIREA – Russian University of Technology (Moscow)

M.Yu. Nilov – Post-graduate Student,

MIREA – Russian University of Technology (Moscow)

Pham Van Ti – Post-graduate Student,

MIREA – Russian University of Technology (Moscow)

The need for a systematic approach to solving the problem of product quality in a market economy is recognized by all, however, the methods of its implementation are very different. An effective way to develop a product development strategy should be based on the synthesis of the latest quality management tools with the achievements of applied mathematical statistics. The task is to develop a strategy for the development of products that allows for a given level of cost to achieve maximum customer satisfaction and Vice versa: at a given level of quality to minimize the costs. One of the most effective methods of quality management is the quality function deployment, which allows to transform the customer's wishes into technical characteristics for the product or service. We propose to develop a model of dependence of costs and customer satisfaction on the levels of engineering characteristics on the basis of factorial experiment, which allows to obtain a model of the objective function (in this case – costs and customer satisfaction) of the influencing factors (in this case – engineering characteristics). Combining the two approaches allows for a trade-off between product development costs and the potential effect of customer satisfaction.

Minimizing costs and maximizing customer satisfaction is achieved by solving the problem of linear programming. The construction of a linear model during the factorial experiment does not require complex calculations and, as a rule, quite accurately describes the dependence of the response on the factors. It is noted that it is possible to build complex regression models of costs and customer satisfaction from the value of engineering characteristics without conducting factor experiments, but based on the results of expert assessments. 

An example of using the proposed method is given and it is noted that the proposed models can be complicated – to use other  experimental plans and to build nonlinear models of costs and customer satisfaction that more accurately characterize the target variables and, in this case, to solve a more complex problem of nonlinear programming.

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